KNOWLEDGE MANAGEMENT SYSTEMS WITH REPUTATION

AND INTUITION

What for?

Juan Pablo Soto, Aurora Vizcaíno, Javier Portillo and Mario Piattini

Alarcos Research Group

Information Systems and Technologies Department, UCLM-Souluziona Research and Development Institute

University of Castilla – La Mancha, Spain

Paseo de la Universidad, 4 – 13071, Ciudad Real, Spain

Keywords: Knowledge management, multi-agent systems, communities of practice, reputation.

Abstract: Nowadays knowledge management is considering to be one of the more important processes by those

companies worried about their competitiveness. These companies focus their efforts on developing systems

that can be used to capture, store and reuse the knowledge generated by theirs employees. Nevertheless, all

this effort may be in vain if the system is not greatly used by the employees because the knowledge that

these systems have is often not valuable or on other occasions the knowledge sources do not provide the

necessary confidence to reuse the information. In an attempt to avoid this situation, we propose a multi-

agent architecture based on communities of practice and on the reputation concept with the purpose of

controlling the utility of information stored in a knowledge base.

1 INTRODUCTION

In recent years knowledge management is a topic of

special interest to organizations who are worried

about their employees’ learning and competitiveness

since a suitable management of this process can help

organizations to increment the collaboration of their

members and encourage the sharing of information

between them. The exchange of information among

employees in an organization represents an

important success factor in improving the

knowledge flow necessary for a suitable knowledge

management. An essential ingredient of knowledge

sharing information in organizations is that of

“community of practice”, by which we mean groups

of people with a common interest where each

member contributes knowledge about a common

domain (Wenger, 1998). This concept has become

more and more popular within the field of the

knowledge management where it is mainly used as a

knowledge management tool to support the

externalization of knowledge, both for reuse as well

as for purposes of innovation (Huysman & Wit,

2000). The importance of the concept of

communities of practice at an organizational level is

parallel to the growth in the interest of management

approaches such as organizational learning and

knowledge management. Communities of practice

enable their members to benefit from each other’s

knowledge. Most of the learning that takes place in

organizations occurs informally in communities of

practice (Lesser, 2000). An interesting fact is that

individuals are frequently more likely to use

knowledge built by their community team members

than those created by members outside their group

(Desouza, 2006). For these reasons, we consider the

modelling of communities of practices into

knowledge management systems an adequate

method by which to provide these systems with a

certain degree of control to measure the confidence

and quality of the information provided for each

member of the community.

In order to carry this out, we have designed a

multi-agent architecture in which agents try to

emulate humans’ rating knowledge sources with the

goal of fostering the use of knowledge bases where

intelligent agents provide “trustworthy knowledge”

to the employees and foster knowledge flow among

them.

The remainder of this work is organized as

follows. The next section presents two important

concepts that take place in the process of obtaining

498

Pablo Soto J., Vizcaíno A., Portillo J. and Piattini M. (2007).

KNOWLEDGE MANAGEMENT SYSTEMS WITH REPUTATION AND INTUITION - What for?.

In Proceedings of the Ninth International Conference on Enterprise Information Systems, pages 498-503

 SciTePress

information (trust and reputation). In section three

the multi-agent architecture proposed to manage

trustworthy knowledge bases is described. In section

four the reputation management used in the agents’

community is presented. In section five we illustrate

how the architecture and reputation management

have been used to implement a prototype which

detects and suggests trustworthy documents for

members in a community of practice. Finally in

section six conclusions are presented.

2 TRUST AND REPUTATION

The main goal of our work is to rate the credibility

of information sources and of knowledge. To do

this, we first need to define two important concepts:

trust and reputation. The former can be defined as

confidence in the ability and intention of an

information source to deliver correct information

(Barber & Kim, 2004) and the latter as the amount

of trust an agent has in an information source,

created through interactions with information

sources. There are other definitions for these

concepts (Gambetta, 1988; Marsh, 1994). However,

we have presented the most appropriate for our

research since the level of confidence in a source is

based on previous experience of this.

Figure 1: Reputation factors.

The reputation of an information source not only

serves as a means of belief revision in a situation of

uncertainty, but also serves as a social law that

obliges us to remain trustworthy to other people.

Therefore, people, in real life in general and in

companies in particular, prefer to exchange

knowledge with “trustworthy people” by which we

mean people they trust. People with a consistently

low reputation will eventually be isolated from the

community since others will rarely accept their

justifications or arguments and will limit their

interaction with them. It is for this reason that the

remainder of this paper deals solely with reputation.

However, if we attempt to imitate the behaviour of

the employees in a company when they are

exchanging and obtaining information we observe

that apart from the concept of reputation other

factors also influence. For this reason, in this paper

we argue that reputation is not a single notion but

one of multiple parts (see Figure 1). These are:

 Position: employees often consider information

that comes from a boss as being more reliable

than that which comes from another employee

in the same (or a lower) position as him/her

(Wasserman & Glaskiewics, 1994). However,

this is not a universal truth and depends on the

situation. For instance in a collaborative

learning setting collaboration is more likely to

occur between people of a similar status than

between a boss and his/her employee or

between a teacher and pupils (Dillenbourg,

1999). Because of this, as will be explained

later, in our research this factor will be

calculated by taking into account a weight that

can strengthen this factor to a greater or to a

lesser degree.

 Expertise: this term can be briefly defined as the

skill or knowledge of a person who knows a

great deal about a specific thing. This is an

important factor since people often trust in

experts more than in novice employees.

Moreover, tools such as expertise location

(Crowder et al, 2002) are being developed with

the goal of promoting the sharing of expertise

knowledge (Rodríguez-Elías et al, 2004).

 Previous experience: People have greater trust

in those sources from which they have

previously obtained more “valuable

information”. Therefore, a factor that influences

the increasing or decreasing reputation of a

source is “previous experience” and this factor

can help us to detect trustworthy sources or

knowledge.

 Intuition: When people do not have a previous

experience they often use their “intuition” to

decide whether or not they are going to trust

something. Other authors have called this issue

“indirect reputation or prior-derived reputation”

(Mui et al, 2002). In human societies, each of us

probably has different prior beliefs about the

trustworthiness of strangers we meet. Sexual or

racial discrimination might be a consequence of

such prior belief (Mui et al, 2002). We have

tried to model intuition according to the

similarity between the user profiles, the greater

the similarity between one agent and another,

the greater the intuition level.

Taking all these factors into account we have

defined an own “concept of reputation”. In

section four we shall describe how we use this

definition to rate knowledge and information

sources.

KNOWLEDGE MANAGEMENT SYSTEMS WITH REPUTATION AND INTUITION - What for?

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3 A MULTIAGENT

ARCHITECTURE TO

DEVELOP TRUSTWORTHY

KNOWLEDGE BASES

When implementing a knowledge management

system we must consider the importance a

knowledge base has within that system. In this work

we have focused our attention on the difficulties of

controlling the quality of the contributions and the

“reputation” of contributors of a knowledge

management system. A knowledge management

system must store only useful knowledge for

employees. However, sometimes the knowledge

which is put into a knowledge base is not very

valuable. This decreases the trust that employees

have in their knowledge bases and reduces the

probability of people using it. In order to avoid this

situation we have developed a multi-agent

architecture in charge of monitoring and evaluating

the knowledge that is stored in a knowledge base.

To design this architecture we have taken into

account how people obtain information in their daily

lives and concretely how this exchange of

information takes place in communities of practice.

Bearing in mind the advantages of working with

groups of similar interests we have organized the

agents into communities of people who are

interested in similar topics. Thus, Figure 2 shows

different communities where there are two types of

agents: the User Agent and the Manager Agent. The

former is used to represent each person that may

consult or introduce knowledge in a knowledge

base.

The User Agent can assume three types of

behaviour or roles similar to the tasks that a person

may carry out in a knowledge base. Therefore, the

User Agent plays one role or another depending

upon whether the person that it represents carries out

one of the following actions:

 The person contributes new knowledge to the

communities in which s/he is registered. In this

case the User Agent plays the role of Provider.

 The person uses knowledge previously stored in

the community. Then, the User Agent will be

considered as a Consumer.

 The person helps other users to achieve their

goals, for instance by giving an evaluation of

certain knowledge. In this case the role is of a

Partner. So, Figure 2 shows that in community

1 there are two User Agents playing the role of

Partner, one User Agent playing the role of

Consumer and another being a Provider.

The fact that this agent can act both as

consumers and also as providers of knowledge may

lead to better results because they aim to motivate

the active participation of the individual in the

learning process, which often results in the

development of creativity and critical thinking (Kan,

1999).

Figure 2: Multi-agent architecture.

The second type of agent within a community is

called the Manager Agent (represented in black in

Figure 2) which is in charge of managing and

controlling its community. In order to accomplish

this, the Manager Agent can perform the following

tasks:

 Registering an agent in its community. It thus

controls how many agents there are and how

long the stay of each agent in that community is.

 Registering the frequency of contribution of

each agent. This value is updated every time an

agent makes a contribution to the community.

 Registering the number of times that an agent

gives feedback about other agents’ knowledge.

For instance, when an agent “A” uses

information from another agent “B”, the agent

A should rate this information. Monitoring how

often an agent gives feedback about other

agents’ information helps to detect whether

agents contribute to the creation of knowledge

flows in the community since it is as important

that an agent contributes with new information

as it is that another agent contributes by

evaluating the relevance or importance of this

information.

 Registering the interactions between agents.

Every time an agent evaluates the contributions

of another agent the Manager Agent will

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only in one direction, which means, if agent A

consults information from agent B and evaluates

it, the Manager records that A knows B but that

does not means that B knows A because B does

not obtain any information about A.

Besides these agents there is another in charge of

initiating new agents and creating new communities.

This agent has two main roles: the “creator” role is

assumed when there is a petition (made by a User

Agent) to create a new Community and the

“initiator” role is assumed when the system is

initially launched. This agent, which is not included

in any of the communities, is located in the centre of

Figure 2, and is called the Creator Agent.

The following section describes how the agent

works in order to obtain reputation values.

4 REPUTATION MANAGEMENT

IN AGENTS’ COMMUNITIES

The idea of using reputation values has two

objectives. The first of them is that agents help

employees to discover the information that is most

relevant for them, thus, decreasing the overload of

information that employees often have and

strengthening the usage of knowledge bases in

companies. Another objective is to avoid the

situation of employees storing valueless information

in a knowledge base. In order to accomplish this

successfully, we need to manage reputation in such a

way that the agents can obtain reputation values that

can be used to maintain the quality of the

information in knowledge bases.

Bearing in mind that the reputation notion

described in section 2 is composed of position,

expertise, previous experiences and intuition, we

will describe the formulas used to measure the level

of reputation in agents’ communities.

For instance, the reputation of agent

in the eyes

of agent

is a collective measure defined by the

previously describe reputation factors in section two

and is computed as follows:

= w

+ w

(∑ QC

)/n

j=1

where R

denotes the reputation value that

agent

has in agent

(each agent in the community

has an opinion about each of the other agent

members of the community).

is the value of expertise which is calculated

according to the degree of experience that a person

has in a domain.

is the value assigned to the position of a

person. This position is defined by the

organizational diagram of the enterprise. Therefore,

a value that determines the hierarchic level within

the organization can be assigned to each level of the

diagram.

is the value assigned to the intuition which is

calculated by comparing the users’ profiles of each

one.

In addition, previous experience should also be

calculated. We suppose that when an agent A

consults information from another agent B, the agent

A should evaluate how useful this information was.

This value is called QC

(Quality of j’s

Contribution). To attain the average value of an

agent’s contribution, we calculate the sum of all the

values assigned to their contributions and we divide

it between their total. In the expression n represents

the total number of evaluated contributions.

Finally, w

, w

and w

are weights with which the

Reputation value can be adjusted to the needs of the

organizations. For instance, if an enterprise

considers that all their employees have the same

category, then w

=0. The same could occur when the

organization does not take its account employees’

intuitions or expertise into account.

In this way, an agent can obtain a value related to

the reputation of another agent and decide to what

degree it is going to consider the information

obtained from this agent.

Moreover, when a user wants to join to a

community in which no member knows anything

about him/her, the reputation value assigned to the

user in the new community is calculated on the basis

of the reputation assigned from others communities

where the user is or was a member. For instance, an

User Agent called j, will ask each community

manager where he/she was previously a member to

consult each agent which knows him/her with the

goal of calculating the average value of his/her

reputation (R

). This is calculated as:

= (∑ R

)/n

i=1

where n is the number agents who know j and R

is the value of j’s reputation in the eyes of i. In the

case of being known in several communities the

average of the values R

will be calculated. Then,

the User Agent j presents this reputation value

(similar to when a person presents his/her

curriculum vitae when s/he wishes to join a

company) to the Manager Agent of the community

to which it is “applying”. This mechanism is similar

to the “word-of-mouth” propagation of information

for a human (Abdul-Rahman & Hailes, 2000).

KNOWLEDGE MANAGEMENT SYSTEMS WITH REPUTATION AND INTUITION - What for?

501

In the case of the user being new in the

community then this user is assigned a “new” label

in order for the situation to be identified.

Once the Community Manager has obtained a

Reputation value for j it is added to the community

member list.

In the following section, we will describe a

prototype developed to validate each of our

proposals.

5 PROTOTYPE

In order to evaluate the architecture and formulas to

manage reputation we have developed a prototype

system into which people can introduce documents

and where these documents can also be consulted by

other people. The goal of this prototype is for agents

software to help employees to discover the

information that may be useful to them thus

decreasing the overload of information that

employees often have and strengthening the use of

knowledge bases in companies. In addition, we try

to avoid the situation of employees storing valueless

information in the knowledge base.

The main feature of this system is that when a

person searches for knowledge in a community, and

after having used the knowledge obtained, that

person then has to evaluate the knowledge.

When a user wants to join to a new community

the person will use a “Register Menu” and choose a

community from all the available communities. In

this case the Manager Agent will ask whether there

are any agents that know new user in order to set a

reputation value on this person.

In addition, the prototype provides the options of

proposing new documents, using community

documents and updating reputation values,

proposing new topics in the community, etc. We

shall now describe only two situations, due to

limitations of space:

1) Proposing new documents. It is assumed that

any person is able to propose documents in those

communities where he/she is a member. To propose

a document a person must use the “Propose Menu”

and will have to configure the followings options:

 Community: The person must select the

community to which s/he proposes to add a

document.

 Topic: In each community there may be

different topics or areas and the user will choose

the one in which s/he intends to propose the

document.

 Document: The proposed document.

 Author: Indicates who the author of the

document is since a person may propose other

authors’ documents. In this case, the proposal is

considered as a contribution but not as the

proposer’s own contribution.

 Knowledge Source: Where the knowledge came

from. It could have come from a partner, from

the person him/herself, from a web page, etc.

Once the user has chosen the options, the User

Agent takes the values and sends them to the

Manager Agent that is in charge of adding the new

document to the community document list and

modifying the frequency of contribution of this

agent in this community.

2) Using community documents and updating

reputation values. People can search for documents

in every community in which they are registered.

When a person searches for a document relating to a

topic his/her User Agent consults the Manager

Agent about which documents are related to their

search. Then, the Manager agent answers with a list

of documents. The User Agent sorts this list

according to the reputation value of the authors,

which is to say that the contributions with the best

reputations for this Agent are listed first. On the

other hand, when the user doesn’t know the

contributor then the User Agent consults the

Manager Agent about which members of the

community know the contributors. Thus, the User

Agent can consult the opinions that other agents

have about these contributors, thus taking advantage

of other agents’ experience. To do this the Manager

consults its interaction table and responds with a list

of the members who know the User Agent Then, this

User Agent contacts each of them. If nobody knows

the contributors then the information is listed, taking

their expertise and positions into account. In this

way the User Agent can detect how worthy a

document is, thus saving employees’ time, since

they do not need to review all the documents related

to a topic but only those considered most relevant by

the members of the community or by the person

him/herself according to previous experience with

the document or its authors.

Once the person has chosen a document, his/her

User Agent adds this document to its own document

list (list of consulted documents), and if the author

of the document is not known by the person because

it is the first time that s/he has worked with him/her,

then the Community Manager adds this relation to

the interaction table. This step is very important

since when the person evaluates the document

consulted, his/her User Agent will be able to assign

a QC for this document.

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6 CONCLUSIONS

The main contribution of this paper is to add a

reputation concept to knowledge bases with the idea

of emulating people’s behaviour within communities

since according to literature the exchange of

knowledge is likely to take place in these

communities thanks to the trust that members have

in each other. Moreover, we have proposed a new

definition of “reputation” which considers aspects

that affect the degree of trust that a person has in

something (a knowledge source, a person, a piece of

knowledge). In this definition intuition, a concept

that according to (Mui et al, 2002) has not yet been

modelled by agent systems has been included.

Another important advantage of our approach is

that we use easy and generic formulas to measure

the reputation in knowledge management systems.

This is very important because our focus may be

useful in several situations.

In addition, this work has illustrated how the

architecture can be used to implement a prototype.

The main functionalities of this architecture are:

- Detecting information which is not particularly

useful in a knowledge base.

- Displaying useful information to employees

according to the user’s profiles.

- Detecting the most important knowledge

sources of a company. Since our approach rates

information as well as the contributor this could

also help companies to detect those employees

with more knowledge about a topic (expert

detection).

This architecture may also be useful in the

implementation of a recommender system as the

better evaluated information can be sent to interested

parties. For instance, our research group will use our

architecture to evaluate research papers and the best

valuated papers will be sent to the members of the

group who work on related topics. In addition the

architecture can be used to support virtual

communities, or to detect the most trustworthy

employees or with the best reputation.

All these situations provide organizations with a

better control of their knowledge bases which will

have more trustworthy knowledge and it is

consequently expected that employees will feel more

willing to use it.

ACKNOWLEDGEMENTS

This work is partially supported by the ENIGMAS

(PIB-05-058), and MECENAS (PBI06-0024)

project,. It is also supported by the ESFINGE project

(TIN2006-15175-C05-05) Ministerio de Educación

y Ciencia (Dirección General de Investigación)/

Fondos Europeos de Desarrollo Regional (FEDER)

in Spain

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